Sebanyak 7 item atau buku ditemukan

Multivariate Reduced-Rank Regression

Theory and Applications

In the area of multivariate analysis, there are two broad themes that have emerged over time. The analysis typically involves exploring the variations in a set of interrelated variables or investigating the simultaneous relation ships between two or more sets of variables. In either case, the themes involve explicit modeling of the relationships or dimension-reduction of the sets of variables. The multivariate regression methodology and its variants are the preferred tools for the parametric modeling and descriptive tools such as principal components or canonical correlations are the tools used for addressing the dimension-reduction issues. Both act as complementary to each other and data analysts typically want to make use of these tools for a thorough analysis of multivariate data. A technique that combines the two broad themes in a natural fashion is the method of reduced-rank regres sion. This method starts with the classical multivariate regression model framework but recognizes the possibility for the reduction in the number of parameters through a restrietion on the rank of the regression coefficient matrix. This feature is attractive because regression methods, whether they are in the context of a single response variable or in the context of several response variables, are popular statistical tools. The technique of reduced rank regression and its encompassing features are the primary focus of this book. The book develops the method of reduced-rank regression starting from the classical multivariate linear regression model.

The technique of reduced rank regression and its encompassing features are the primary focus of this book. The book develops the method of reduced-rank regression starting from the classical multivariate linear regression model.

Decomposition Methodology for Knowledge Discovery and Data Mining

Theory and Applications

Data Mining is the science and technology of exploring data in order to discover previously unknown patterns. It is a part of the overall process of Knowledge Discovery in Databases (KDD). The accessibility and abundance of information today makes data mining a matter of considerable importance and necessity. This book provides an introduction to the field with an emphasis on advanced decomposition methods in general data mining tasks and for classification tasks in particular. The book presents a complete methodology for decomposing classification problems into smaller and more manageable sub-problems that are solvable by using existing tools. The various elements are then joined together to solve the initial problem.The benefits of decomposition methodology in data mining include: increased performance (classification accuracy); conceptual simplification of the problem; enhanced feasibility for huge databases; clearer and more comprehensible results; reduced runtime by solving smaller problems and by using parallel/distributed computation; and the opportunity of using different techniques for individual sub-problems.

This book provides an introduction to the field with an emphasis on advanced decomposition methods in general data mining tasks and for classification tasks in particular.

Data Mining with Decision Trees

Theory and Applications

This is the first comprehensive book dedicated entirely to the field of decision trees in data mining and covers all aspects of this important technique.Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining, the science and technology of exploring large and complex bodies of data in order to discover useful patterns. The area is of great importance because it enables modeling and knowledge extraction from the abundance of data available. Both theoreticians and practitioners are continually seeking techniques to make the process more efficient, cost-effective and accurate. Decision trees, originally implemented in decision theory and statistics, are highly effective tools in other areas such as data mining, text mining, information extraction, machine learning, and pattern recognition. This book invites readers to explore the many benefits in data mining that decision trees offer: Self-explanatory and easy to follow when compacted Able to handle a variety of input data: nominal, numeric and textual Able to process datasets that may have errors or missing values High predictive performance for a relatively small computational effort Available in many data mining packages over a variety of platforms Useful for various tasks, such as classification, regression, clustering and feature selection

This is the first comprehensive book dedicated entirely to the field of decision trees in data mining and covers all aspects of this important technique.Decision trees have become one of the most powerful and popular approaches in knowledge ...

Input-output Economics

Theory and Applications : Featuring Asian Economies

Thijs ten Raa, author of the acclaimed text The Economics of InputOCoOutput Analysis, now takes the reader to the forefront of the field. This volume collects and unifies his and his co-authors'' research papers on national accounting, InputOCoOutput coefficients, economic theory, dynamic models, stochastic analysis, and performance analysis. The research is driven by the task to analyze national economies. The final part of the book scrutinizes the emerging Asian economies in the light of international competition. Sample Chapter(s). Introduction (45 KB). Chapter 1: National Accounts, Planning and Prices (108 KB). Contents: National Accounts: National Accounts, Planning and Prices; Commodity and Sector Classifications in Linked Systems of National Accounts; Accounting or Technical Coefficients: The Choice of Model in the Construction of InputOCoOutput Coefficients Matrices; The Extraction of Technical Coefficients from Input and Output Data; Neoclassical and Classical Connections: On the Methodology of InputOCoOutput Analysis; The Substitution Theorem; Dynamic InputOCoOutput Analysis: Dynamic InputOCoOutput Analysis with Distributed Activities; Applied Dynamic InputOCoOutput with Distributed Activities; Stochastic InputOCoOutput Analysis: Primary Versus Secondary Production Techniques in US Manufacturing; Stochastic Analysis of InputOCoOutput Multipliers on the Basis of Use and Make Tables; Performance Analysis: A Neoclassical Analysis of TFP Using InputOCoOutput Prices; Competition and Performance: The Different Roles of Capital and Labor; The Canadian Economy: A General Equilibrium Analysis of the Evolution of Canadian Service Productivity; The Location of Comparative Advantages on the Basis of Fundamentals Only; Asian Economies: Competitive Pressures on China: Income Inequality and Migration; Competitive Pressure on the Indian Households: A General Equilibrium Approach; and other papers. Readership: Economists at research institutes and universities, national accountants, graduate students in economics, and trade policy analysts."

This volume collects and unifies his and his co-authors'' research papers on national accounting, InputOCoOutput coefficients, economic theory, dynamic models, stochastic analysis, and performance analysis.

Bilateral Filtering

Theory and Applications

Bilateral filtering is one of the most popular image processing techniques. The bilateral filter is a nonlinear process that can blur an image while respecting strong edges. Its ability to decompose an image into different scales without causing haloes after modification has made it ubiquitous in computational photography applications such as tone mapping, style transfer, relighting, and denoising. Bilateral Filtering: Theory and Applications provides a graphical, intuitive introduction to bilateral filtering, a practical guide for efficient implementation, an overview of its numerous applications, as well as mathematical analysis. This broad and detailed overview covers theoretical and practical issues that will be useful to researchers and software developers.

Bilateral filtering is one of the most popular image processing techniques.

Fibonacci and Lucas Numbers, and the Golden Section

Theory and Applications

This survey of the use of Fibonacci and Lucas numbers and the ancient principle of the Golden Section covers areas relevant to operational research, statistics, and computational mathematics. 1989 edition.

This survey of the use of Fibonacci and Lucas numbers and the ancient principle of the Golden Section covers areas relevant to operational research, statistics, and computational mathematics. 1989 edition.

Digital Imaging

Theory and Applications

From one of this burgeoning field's true pioneers, here is a much-needed guide to digital image processing that is both authoritative and accessible. Howard Burdick's book/CD-ROM package delivers the basic knowledge and the sample programs you need to utilize digital imaging techniques in a wide variety of real-world situations. More than just another technical cookbook weighed down by mathematical abstractions, Digital Imaging paints a complete picture of the subject in terms anyone can understand. The accompanying CD-ROM provides many complete programming examples, plus sample images that serve to illustrate an array of practical algorithms and processing techniques ... and provide a springboard from which to create your own applications.

From one of this burgeoning field's true pioneers, here is a much-needed guide to digital image processing that is both authoritative and accessible.